# Opinion Maximization in Social Networks

arXiv: https://arxiv.org/abs/1301.7455 · HTML: https://mlsanity.com/paper/1301.7455/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Jan 2013.

## Built on by (most cited first, 6 of 6)

- [GraMeR: Graph Meta Reinforcement Learning for Multi-Objective Influence Maximization](https://mlsanity.com/paper/2205.14834/) (2022)
- [Reducing Exposure to Harmful Content via Graph Rewiring](https://mlsanity.com/paper/2306.07930/) (2023)
- [Adversaries with Limited Information in the Friedkin--Johnsen Model](https://mlsanity.com/paper/2306.10313/) (2023)
- [AI-Mediated Communication Can Steer Collective Opinion](https://mlsanity.com/paper/2605.16245/) (2026)
- [Digital Persuasion: Understanding the Impact of Online Influencers on Public Opinion](https://mlsanity.com/paper/2609.16062/) (2026)
- [Is Monitoring Enough? Strategic Agent Selection For Stealthy Attack in Multi-Agent Discussions](https://mlsanity.com/paper/2603.21194/) (2026)

## Abstract (arXiv)

The process of opinion formation through synthesis and contrast of different viewpoints has been the subject of many studies in economics and social sciences. Today, this process manifests itself also in online social networks and social media. The key characteristic of successful promotion campaigns is that they take into consideration such opinion-formation dynamics in order to create a overall favorable opinion about a specific information item, such as a person, a product, or an idea. In this paper, we adopt a well-established model for social-opinion dynamics and formalize the campaign-design problem as the problem of identifying a set of target individuals whose positive opinion about an information item will maximize the overall positive opinion for the item in the social network. We call this problem CAMPAIGN. We study the complexity of the CAMPAIGN problem, and design algorithms for solving it. Our experiments on real data demonstrate the efficiency and practical utility of our algorithms.